class_docstr: Implementation of Dataset class
  
  This is a simple implementation of loading and processing dataset for GATNE model.
  
  Args:
      config: dict, some configure parameters.
class_docstr_tok: ["Implementation","of","Dataset","class","This","is","a","simple","implementation","of","loading","and","processing","dataset","for","GATNE","model",".","Args",":","config",":","dict",",","some","configure","parameters","."]
docstr: Generate random walks in specified edge type.
          
docstr_tok: ["Generate","random","walks","in","specified","edge","type","."]
code: def simulate_walks(
      self, edge_type, num_walks, walk_length, schema=None
  ):
      """Generate random walks in specified edge type."""
      walks = []
      nodes = list(range(0, self.graph[edge_type].num_nodes))
  
      for walk_iter in tqdm.tqdm(range(num_walks)):
          random.shuffle(nodes)
          for node in nodes:
              walk = self.graph[edge_type].random_walk(
                  [node], max_depth=walk_length - 1
              )
              for i in range(len(walk)):
                  walks.append(walk[i])
  
      return walks
  
file_path: PGL/legacy/examples/GATNE/Dataset.py
imports: import sys
  import os
  import tqdm
  import numpy
  import logging
  import random
  from pgl import heter_graph
  import pickle
label_desc: Use image node_id 7 for calling the Dataset obj's underlying member method code with example usage: obj.simulate_walks(edge_type, num_walks, walk_length, schema) and returns: walks